Biosensor systems provide an analysis of a biological fluid sample, such as whole blood, serum, plasma, urine, saliva, interstitial, or intracellular fluid. Typically, the systems include a measurement device that analyzes a sample residing in a test sensor. The sample usually is in liquid form and in addition to being a biological fluid, may be the derivative of a biological fluid, such as an extract, a dilution, a filtrate, or a reconstituted precipitate. The analysis performed by the biosensor system determines the presence and/or concentration of one or more analytes, such as alcohol, glucose, uric acid, lactate, cholesterol, bilirubin, free fatty acids, triglycerides, proteins, ketones, phenylalanine or enzymes, in the biological fluid. The analysis may be useful in the diagnosis and treatment of physiological abnormalities. For example, a diabetic individual may use a biosensor system to determine the glucose level in whole blood for adjustments to diet and/or medication.
Biosensor systems may be designed to analyze one or more analytes and may use different volumes of biological fluids. Some systems may analyze a single drop of whole blood, such as from 0.25-15 microliters (μL) in volume. Biosensor systems may be implemented using bench-top, portable, and like measurement devices. Portable measurement devices may be hand-held and allow for the identification and/or quantification of one or more analytes in a sample. Examples of portable measurement systems include the Elite® meters of Bayer HealthCare in Tarrytown, N.Y., while examples of bench-top measurement systems include the Electrochemical Workstation available from CH Instruments in Austin, Tex.
Biosensor systems may use optical and/or electrochemical methods to analyze the biological fluid. In some optical systems, the analyte concentration is determined by measuring light that has interacted with or been absorbed by a light-identifiable species, such as the analyte or a reaction or product formed from a chemical indicator reacting with the analyte. In other optical systems, a chemical indicator fluoresces or emits light in response to the analyte when illuminated by an excitation beam. The light may be converted into an electrical output signal, such as current or potential, which may be similarly processed to the output signal from an electrochemical system. In either optical system, the system measures and correlates the light with the analyte concentration of the sample.
In light-absorption optical systems, the chemical indicator produces a reaction product that absorbs light. A chemical indicator such as tetrazolium along with an enzyme such as diaphorase may be used. Tetrazolium usually forms formazan (a chromagen) in response to the redox reaction of the analyte. An incident input beam from a light source is directed toward the sample. The light source may be a laser, a light emitting diode, or the like. The incident beam may have a wavelength selected for absorption by the reaction product. As the incident beam passes through the sample, the reaction product absorbs a portion of the incident beam, thus attenuating or reducing the intensity of the incident beam. The incident beam may be reflected back from or transmitted through the sample to a detector. The detector collects and measures the attenuated incident beam (output signal). The amount of light attenuated by the reaction product is an indication of the analyte concentration in the sample.
In light-generated optical systems, the chemical detector fluoresces or emits light in response to the analyte redox reaction. A detector collects and measures the generated light (output signal). The amount of light produced by the chemical indicator is an indication of the analyte concentration in the sample.
In electrochemical biosensor systems, the analyte concentration is determined from an electrical signal generated by an oxidation/reduction or redox reaction of the analyte or a species responsive to the analyte when an input signal is applied to the sample. The input signal may be a potential or current and may be constant, variable, or a combination thereof such as when an AC signal is applied with a DC signal offset. The input signal may be applied as a single pulse or in multiple pulses, sequences, or cycles. An enzyme or similar species may be added to the sample to enhance the electron transfer from a first species to a second species during the redox reaction. The enzyme or similar species may react with a single analyte, thus providing specificity to a portion of the generated output signal. A mediator may be used to maintain the oxidation state of the enzyme and/or assist with electron transfer from the analyte to an electrode.
Electrochemical biosensor systems usually include a measurement device having electrical contacts that connect with the electrical conductors of the test sensor. The conductors may be made from conductive materials, such as solid metals, metal pastes, conductive carbon, conductive carbon pastes, conductive polymers, and the like. The electrical conductors typically connect to working, counter, reference, and/or other electrodes that extend into a sample reservoir. One or more electrical conductors also may extend into the sample reservoir to provide functionality not provided by the electrodes.
The measurement device applies an input signal through the electrical contacts to the electrical conductors of the test sensor. The electrical conductors convey the input signal through the electrodes into the sample present in the sample reservoir. The redox reaction of the analyte generates an electrical output signal in response to the input signal. The electrical output signal from the test sensor may be a current (as generated by amperometry or voltammetry), a potential (as generated by potentiometry/galvanometry), or an accumulated charge (as generated by coulometry). The measurement device may have the processing capability to measure and correlate the output signal with the presence and/or concentration of one or more analytes in the sample.
In coulometry, a potential is applied to the sample to exhaustively oxidize or reduce the analyte. A biosensor system using coulometry is described in U.S. Pat. No. 6,120,676. In amperometry, an electrical signal of constant potential (voltage) is applied to the electrical conductors of the test sensor while the measured output signal is a current. Biosensor systems using amperometry are described in U.S. Pat. Nos. 5,620,579; 5,653,863; 6,153,069; and 6,413,411. In voltammetry, an electric signal of varying potential is applied to a sample of biological fluid, while the measured output is current. In gated amperometry and gated voltammetry, pulsed inputs are used as described in WO 2007/013915 and WO 2007/040913, respectively.
In many biosensor systems, the test sensor may be adapted for use outside, inside, or partially inside a living organism. When used outside a living organism, a sample of the biological fluid may be introduced into a sample reservoir in the test sensor. The test sensor may be placed in the measurement device before, after, or during the introduction of the sample for analysis. When inside or partially inside a living organism, the test sensor may be continually immersed in the sample or the sample may be intermittently introduced to the test sensor. The test sensor may include a reservoir that partially isolates a volume of the sample or be open to the sample. When open, the test sensor may take the form of a fiber or other structure placed in contact with the biological fluid. Similarly, the sample may continuously flow through the test sensor, such as for continuous monitoring, or be interrupted, such as for intermittent monitoring, for analysis.
The measurement performance of a biosensor system is defined in terms of accuracy, which reflects the combined effects of random and systematic error components. Systematic error, or trueness, is the difference between the average value determined from the biosensor system and one or more accepted reference values for the analyte concentration of the biological fluid. Trueness may be expressed in terms of mean bias, with larger mean bias values representing lower trueness and thereby contributing to less accuracy. Precision is the closeness of agreement among multiple analyte readings in relation to a mean. One or more errors in the analysis contribute to the bias and/or imprecision of the analyte concentration determined by the biosensor system. A reduction in the analysis error of a biosensor system therefore leads to an increase in accuracy and thus an improvement in measurement performance.
Bias may be expressed in terms of “absolute bias” or “percent bias”. Absolute bias may be expressed in the units of the measurement, such as mg/dL, while percent bias may be expressed as a percentage of the absolute bias value over 100 mg/dL or the reference analyte concentration of the sample. For glucose concentrations less than 100 mg/dL, percent bias is defined as (the absolute bias over 100 mg/dL)*100. For glucose concentrations of 100 mg/dL and higher, percent bias is defined as the absolute bias over the reference analyte concentration*100. Accepted reference values for the analyte glucose in whole blood samples may be obtained with a reference instrument, such as the YSI 2300 STAT PLUS™ available from YSI Inc., Yellow Springs, Ohio. Other reference instruments and ways to determine percent bias may be used for other analytes.
Hematocrit bias refers to the average difference (systematic error) between the reference glucose concentration obtained with a reference instrument and experimental glucose readings obtained from a biosensor system for samples containing differing hematocrit levels. The difference between the reference and values obtained from the system results from the varying hematocrit level between specific whole blood samples and may be generally expressed as a percentage by the following equation: % Hct-Bias=100%×(Gm−Gref)/Gref, where Gm is the determined glucose concentration at a specific hematocrit level and Gref is the reference glucose concentration at a reference hematocrit level. The larger the absolute value of the % Hct-bias, the more the hematocrit level of the sample (expressed as % Hct, the percentage of red blood cell volume/sample volume) is reducing the accuracy of the determined glucose concentration.
For example, if whole blood samples containing identical glucose concentrations, but having hematocrit levels of 20, 40, and 60%, are analyzed, three different glucose concentrations will be reported by a system based on one set of calibration constants (slope and intercept of the 40% hematocrit containing whole blood sample, for instance). Thus, even though the whole blood glucose concentrations are the same, the system will report that the 20% hematocrit sample contains more glucose than the 40% hematocrit sample, and that the 60% hematocrit sample contains less glucose than the 40% hematocrit sample. “Hematocrit sensitivity” is an expression of the degree to which changes in the hematocrit level of a sample affect the bias values for an analysis. Hematocrit sensitivity may be defined as the numerical values of the percent biases per percent hematocrit, thus bias/%-bias per % Hct.
Biosensor systems may provide an output signal during the analysis of the biological fluid that includes errors from multiple error sources. These error sources contribute to the total error, which may be reflected in an abnormal output signal, such as when one or more portions or the entire output signal is non-responsive or improperly responsive to the analyte concentration of the sample.
These errors may be from one or more contributors, such as the physical characteristics of the sample, the environmental aspects of the sample, the operating conditions of the system, the manufacturing variation between test sensor lots, and the like. Physical characteristics of the sample include hematocrit (red blood cell) concentration, interfering substances, such as lipids and proteins, and the like. Interfering substances include ascorbic acid, uric acid, acetaminophen, and the like. Environmental aspects of the sample include temperature and the like. Operating conditions of the system include underfill conditions when the sample size is not large enough, slow-filling of the sample, intermittent electrical contact between the sample and one or more electrodes in the test sensor, prior degradation of the reagents that interact with the analyte, and the like. Manufacturing variations between test sensor lots include changes in the amount and/or activity of the reagents, changes in the electrode area and/or spacing, changes in the electrical conductivity of the conductors and electrodes, and the like. A test sensor lot is preferably made in a single manufacturing run where lot-to-lot manufacturing variation is substantially reduced or eliminated. Manufacturing variations also may be introduced as the activity of the reagents changes or degrades between the time the test sensor is manufactured and when it is used for an analysis. There may be other contributors or a combination of contributors that cause errors in the analysis.
Percent bias limit, percent bias standard deviation, average percent bias standard deviation, mean percent bias spread, and hematocrit sensitivity are independent ways to express the measurement performance of a biosensor system. Additional ways may be used to express the measurement performance of a biosensor system.
Percent bias limits are a representation of the accuracy of the biosensor system in relation to a reference analyte concentration, while the percent bias standard deviation and average percent bias standard deviation reflect the precision achieved across multiple test sensors of a single or of multiple manufacturing lots, respectively, with regard to errors arising from the physical characteristics of the sample, the environmental aspects of the sample, and the operating conditions of the system. Mean percent bias spread (the distance of the mean percent bias of a single lot from the mean of the mean percent bias of two or more test sensor lots) reflects the closeness of the analyte concentrations determined from the test sensors of two or more test sensor lots for the same analyte concentration in view of the manufacturing variation between the lots.
The percent of analyses that fall within a “percent bias limit” of a selected percent bias boundary indicate the percent of the determined analyte concentrations that are close to a reference concentration. Thus, the limit defines how close the determined analyte concentrations are to the reference concentration. For instance, 95 out of 100 performed analysis (95%) falling within a ±10% percent bias limit is a more accurate result than 80 out of 100 performed analysis (80%) falling within a ±10% percent bias limit. Thus, an increase in the percentage of analyses falling within a selected percent bias limit represents an increase in the measurement performance of the biosensor system.
The mean may be determined for the percent biases determined from multiple analyses using test sensors from a single lot to provide a “mean percent bias” for the multiple analyses. The mean percent bias may be determined for a single lot of test sensors by using a subset of the lot, such as 100-140 test sensors, to analyze multiple blood samples. As a mean percent bias may be determined for a single lot of test sensors, a “percent bias standard deviation” also may be determined to describe how far the percent bias from an individual analysis is away from the mean percent bias of the test sensor lot. Percent bias standard deviation may be considered an indicator of the precision of a single analysis in relation to the mean of multiple analyses from the same test sensor lot. These percent bias standard deviation values may be averaged, such as arithmetically, using root mean squares, or by other means, to provide an indicator of the precision of a single analysis in relation to the mean of multiple analyses from multiple test sensor lots. Thus, a decrease in percent bias standard deviation or the average percent bias standard deviation represents an increase in the measurement performance of the biosensor system in relation to a single test sensor lot or multiple test sensor lots, respectively.
The mean may be determined for the mean percent biases determined from multiple analyses using test sensors from multiple lots to provide a “grand mean percent bias” for the multiple lots. The grand mean percent bias may be determined for two or more lots of test sensors. As a grand mean percent bias may be determined for multiple lots of test sensors, a “mean percent bias spread” also may be determined to describe how far the mean percent bias from an individual test sensor lot is away from the grand mean percent bias of multiple test sensor lots. Mean percent bias spread may be considered an indicator of the precision of a single test sensor lot in relation to the mean of the mean of multiple analyses from multiple test sensor lots. Thus, a decrease in mean percent bias spread represents an increase in the measurement performance of the biosensor system in relation to manufacturing variations from multiple test sensor lots and an increase in the precision achieved across multiple test sensors from multiple manufacturing lots with regard to errors arising from the manufacturing variation between the lots.
Increasing the measurement performance of the biosensor system by reducing errors from these or other sources means that more of the analyte concentrations determined by the biosensor system may be used for accurate therapy by the patient when blood glucose is being monitored, for example. Additionally, the need to discard test sensors and repeat the analysis by the patient also may be reduced.
A test case is a collection of multiple analyses (data population) arising under substantially the same testing conditions using test sensors from the same lot. For example, determined analyte concentration values have typically exhibited poorer measurement performance for user self-testing than for health care professional (“HCP”) testing and poorer measurement performance for HCP-testing than for controlled environment testing. This difference in measurement performance may be reflected in larger percent bias standard deviations for analyte concentrations determined through user self-testing than for analyte concentrations determined through HCP-testing or through controlled environment testing. A controlled environment is an environment where physical characteristics and environmental aspects of the sample may be controlled, preferably a laboratory setting. Thus, in a controlled environment, hematocrit concentrations can be fixed and actual sample temperatures can be known and compensated. In a HCP test case, operating condition errors may be reduced or eliminated. In a user self-testing test case, such as a clinical trial, the determined analyte concentrations likely will include error from all types of error sources.
Biosensor systems may have a single source of uncorrected output values responsive to a redox or light-based reaction of the analyte, such as the counter and working electrodes of an electrochemical system. Biosensor systems also may have the optional ability to determine or estimate temperature, such as with one or more thermocouples or other means. In addition to these systems, biosensor systems also may have the ability to generate additional output values external to those from the analyte or from a mediator responsive to the analyte. For example, in an electrochemical test sensor, one or more electrical conductors also may extend into the sample reservoir to provide functionality not provided by the working and counter electrodes. Such conductors may lack one or more of the working electrode reagents, such as the mediator, thus allowing for the subtraction of a background interferent signal from the working electrode signal.
Many biosensor systems include one or more methods to compensate for errors associated with an analysis, thus attempting to improve the measurement performance of the biosensor system. Compensation methods may increase the measurement performance of a biosensor system by providing the biosensor system with the ability to compensate for inaccurate analyses, thus increasing the accuracy and/or precision of the concentration values obtained from the system. Conventional error compensation methods for physical and environmental error contributors are traditionally developed in a laboratory as these types of errors can be reproduced in a controlled environment. However, operating condition error contributors are less readily reproduced in the laboratory as many of these errors arise from the way in which the user operates the biosensor system. Thus, errors arising from operating errors may be difficult to reproduce in a laboratory setting and thus difficult to compensate with a conventional compensation method.
Accordingly, there is an ongoing need for improved biosensor systems, especially those that may provide increasingly accurate determination of sample analyte concentrations when operating condition errors are introduced into the analysis by user self-testing. The systems, devices, and methods of the present invention overcome at least one of the disadvantages associated with conventional biosensor systems.